The development of technology has brought in a high-quality and high-speed network communication, and a variety of electronic devices having a network assessing function is being innovated. Therefore, over the years, the market transaction amount in the e-commerce has greatly grown up, and then a great deal of relevant firms has deployed their markets in the e-commerce.
However, these firms also face a challenge to the accurate handling of the customer's tendency in the e-commerce. For example, modern methods used in the art include: gathering statistics of hot produces, gathering statistics of the distribution of customers (including time and positions), calculating the conversion rate in a preset goal stage, analyzing the effect of a specific promotion activity, etc. These modern methods cannot accurately survey the customer's behavior yet.
In addition, modern methods of web analytics usually compute statistics of a complete clickstream. The complete clickstream, however, may have many data sections about a user's actions. For example, users may browse the contents of web pages purposelessly, compare products, purchase products, or edit their member information. Therefore, the complete clickstream may have a great deal of useless contents. Moreover, a behavior model frequently appearing in the complete clickstream does not mean that it is more useful.
Accordingly, it actually requires specialists in data scientist to check the contents of the complete clickstream one by one, so as to find out a more useful behavior model. This conventional way greatly depends on human experiences and thus, has a relatively low efficiency.